{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:46:12Z","timestamp":1769820372535,"version":"3.49.0"},"reference-count":33,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,6,1]]},"abstract":"<jats:p>This study presents a fault diagnosis method for rolling bearing based on multi-scale deep subdomain adaptation network (MSDSAN). The proposed MSDSAN, as improvement of deep subdomain adaptation network (DSAN), is an unsupervised transfer learning method. MSDSAN reduces the subdomain distribution discrepancy between domains rather than marginal distribution discrepancy, and so better domain invariant fault features are derived to avoid misalignment between domains. Aiming at avoiding fault information loss by fixed receptive fields feature extraction, selective kernel convolution module is introduced into feature extraction of MSDSAN, by which multiple receptive fields are applied to ensure an optimal receptive field for each working condition. Moreover, contribution rates are adaptively assigned to all receptive fields, and the disturbing information extracted by inappropriate receptive fields is further eliminated. As a result, more comprehensive and effective fault information is derived for bearing fault diagnosis. Fault diagnosis experiment of bearings is performed to verify the superiority of the proposed method, and the experimental results demonstrate that MSDSAN achieves better transfer effects and higher accuracy than SOTA methods under varying working conditions.<\/jats:p>","DOI":"10.3233\/jifs-212343","type":"journal-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T11:45:25Z","timestamp":1641555925000},"page":"575-585","source":"Crossref","is-referenced-by-count":3,"title":["Fault diagnosis of rolling bearings based on multi-scale deep subdomain adaptation network"],"prefix":"10.1177","volume":"43","author":[{"given":"Qin","family":"Zhou","sequence":"first","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China"}]},{"given":"Zuqiang","family":"Su","sequence":"additional","affiliation":[{"name":"School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China"}]},{"given":"Lanhui","family":"Liu","sequence":"additional","affiliation":[{"name":"Chongqing Industrial Big Data Innovation Center Co., Ltd, Chongqing, China"}]},{"given":"Xiaolin","family":"Hu","sequence":"additional","affiliation":[{"name":"Chongqing Industrial Big Data Innovation Center Co., Ltd, Chongqing, China"}]},{"given":"Jianhang","family":"Yu","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-212343_ref1","doi-asserted-by":"crossref","first-page":"109552","DOI":"10.1016\/j.measurement.2021.109552","article-title":"Fault diagnosis of rolling bearing based on multiscale one-dimensional hybrid binary pattern","volume":"181","author":"Cao","year":"2021","journal-title":"Measurement"},{"key":"10.3233\/JIFS-212343_ref2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.neucom.2020.04.073","article-title":"Deep multi-scale convolutional transfer learning network: A novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains","volume":"407","author":"Zhao","year":"2020","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-212343_ref3","doi-asserted-by":"crossref","first-page":"1848","DOI":"10.1109\/ACCESS.2018.2886343","article-title":"Deep Decoupling Convolutional Neural Network for Intelligent Compound Fault Diagnosis","volume":"7","author":"Huang","year":"2019","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-212343_ref4","doi-asserted-by":"crossref","first-page":"107050","DOI":"10.1016\/j.ress.2020.107050","article-title":"Multi-scale deep intra-class transfer learning for bearing fault diagnosis","volume":"202","author":"Wang","year":"2020","journal-title":"Reliab. 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